Reinforcement Learning papers, 2023–2026

Key findings

• Reinforcement Learning went from 1,070 papers in 2023 to 2,985 in 2026, roughly 2.8x in three years.
• NeurIPS publishes the most Reinforcement Learning work: 2,494 papers from 2023 to 2026, 36% of the four-venue total.
• The fastest-growing overlap is with Reasoning Models: papers tagged with both rose from 9 in 2023 to 437 in 2026.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026\texttt{FEROM}: Frontier Endogenous Reveal-Order Marginal Policy Optimization for Masked Diffusion LMs
• ICML 2026$\text{DT}^\text{2}$: Decision-Targeted Digital Twins
• ICLR 2026$\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Textual Space
• CVPR 2026A Causal Marriage between VLM and IRM from Understanding to
• NeurIPS 2025$\texttt{G1}$: Teaching LLMs to Reason on Graphs with Reinforcement Learning
• ICML 2025A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
• ICLR 2025$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
• CVPR 2025All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
• NeurIPS 2024A Critical Evaluation of AI Feedback for Aligning Large Language Models
• ICML 2024${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
• ICLR 2024A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
• CVPR 2024DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning
• NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
• ICML 2023A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
• ICLR 2023$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
• CVPR 2023"Seeing" Electric Network Frequency From Events
• NeurIPS 2026$\varphi$TD: Distributional Reinforcement Learning using Characteristic Functions
• ICML 2026$\textit{S}$-SPPO: Semantic-Calibrated Self-Play Preference Optimization
• ICLR 2026$AutoDrive\text{-}P^3$: Unified Chain of Perception–Prediction–Planning Thought via Reinforcement Fine-Tuning
• CVPR 2026AdaptVision Efficient Vision-Language Models via Adaptive Visual Acquisition

All Reinforcement Learning papers

2,985 papers, page 55 of 60

2701. NeurIPS 2026PhysTacGen: Physics-Aware Visual-Tactile Sensor Image Generation
2702. NeurIPS 2026PiCA: Pivot-Based Credit Assignment For Search Agentic Reinforcement Learning
2703. NeurIPS 2026Plan2Sense: Open-World Task Planning in Epistemic States via Interleaved Ontic and Sensing Actions
2704. NeurIPS 2026PLATO: Pointer Learner for Agent and Task Openness
2705. NeurIPS 2026Plausible Biomolecular Structure Prediction via Physics-informed Reinforcement Learning
2706. NeurIPS 2026PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
2707. NeurIPS 2026PoEM: Predicting New RL Outcomes from Existing Policies
2708. NeurIPS 2026Policy Gradient Algorithms in Average-Reward Multichain MDPs
2709. NeurIPS 2026Policy-DRIFT: Dynamic Reward-Informed Flow Trajectory Steering
2710. NeurIPS 2026Policy-Level Exploration for Coordinated Multi-Agent Reinforcement Learning
2711. NeurIPS 2026Posterior Sampling-based Online Learning for Episodic POMDPs
2712. NeurIPS 2026Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
2713. NeurIPS 2026PRECISE: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models
2714. NeurIPS 2026Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization
2715. NeurIPS 2026PreFT: Prefill-only finetuning for inference efficiency
2716. NeurIPS 2026Preserving Exploration for LLM Reasoning via Mean Order-Statistic Alignment
2717. NeurIPS 2026Prevailing Bisimulation Metric Learning Is Biased: Implicit Regularization and Its Remedy
2718. NeurIPS 2026PriFT: Prior-Support Guided Token Reweighting for Supervised Fine-Tuning
2719. NeurIPS 2026Principled Policy Optimization for LLMs via Self-Normalized Importance Sampling
2720. NeurIPS 2026ProgramBench: Can Language Models Rebuild Programs From Scratch?
2721. NeurIPS 2026Programmatic Reasoning with Structural Schema: A Unified Framework for Multi-Table Inference
2722. NeurIPS 2026Proportionality in Ranking Compression
2723. NeurIPS 2026Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction–Reality Gaps
2724. NeurIPS 2026Provably Safe, Yet Performant Reinforcement Learning
2725. NeurIPS 2026Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization
2726. NeurIPS 2026Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching
2727. NeurIPS 2026Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing
2728. NeurIPS 2026QMaxCal: Path-Space Regularization for Open Quantum Control via Girsanov's Theorem
2729. NeurIPS 2026Quantile Geometry Regularization for Distributional Reinforcement Learning
2730. NeurIPS 2026Quantile-Coupled Flow Matching for Distributional Reinforcement Learning
2731. NeurIPS 2026Quest: Training Frontier Deep Research Agents with Fully Synthetic Tasks
2732. NeurIPS 2026Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
2733. NeurIPS 2026RAHF: Reward-Amplified Human Feedback for Closed-Loop Policy Fine-Tuning
2734. NeurIPS 2026Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding
2735. NeurIPS 2026Read It Back: Pretrained MLLMs Are Zero-shot Reward Models for Text-to-Image Generation
2736. NeurIPS 2026Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
2737. NeurIPS 2026Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck
2738. NeurIPS 2026Reasoning Warm-up: Scaling Label-free RL via Verifiable Surrogate Rewards
2739. NeurIPS 2026Reasoning with Sampling: Cutting at Decision Points
2740. NeurIPS 2026RECAP: Looking Once Is Not Enough for Vision-Language Reasoning
2741. NeurIPS 2026ReCon: Toward Balanced Learning under Inter-Context and Context-Memory Conflicts
2742. NeurIPS 2026Reconciling Operational Energy Trilemma: A Heterogeneous Risk-Constrained MDP Framework with Residual Policy Learning
2743. NeurIPS 2026Reconciling Safety and Performance via Dual-Expert Offline Imitation Learning
2744. NeurIPS 2026Rectified Policy Rollouts with Hierarchical Expert Guidance for Neural Combinatorial Optimization
2745. NeurIPS 2026ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving
2746. NeurIPS 2026Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias
2747. NeurIPS 2026ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
2748. NeurIPS 2026Reinforcement Learning Agents Are Swimmers
2749. NeurIPS 2026Reinforcement learning enhanced flow matching for reference-based tumor generation on unpaired CT images
2750. NeurIPS 2026Reinforcement Learning for Code Optimization

FAQ

How many Reinforcement Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

6,885 in total: 1,070 in 2023, 1,190 in 2024, 1,640 in 2025, 2,985 in 2026.

How many Reinforcement Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 760 Reinforcement Learning papers, versus 784 the year before.

How many Reinforcement Learning papers are at ICLR 2026?

ICLR 2026 has 911 Reinforcement Learning papers, versus 379 the year before.

How many Reinforcement Learning papers are at ICML 2026?

ICML 2026 has 1,021 Reinforcement Learning papers, versus 405 the year before.

How many Reinforcement Learning papers are at CVPR 2026?

CVPR 2026 has 293 Reinforcement Learning papers, versus 72 the year before.

Which conference publishes the most Reinforcement Learning papers?

NeurIPS, with 2,494 papers from 2023 to 2026.